Source code for modelzoo.vision.pytorch.dit.layers.vae.VAEDecoder

# Copyright 2022 Cerebras Systems.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Copyright 2023 The HuggingFace Team. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/vae.py


import torch.nn as nn

from modelzoo.vision.pytorch.dit.layers.vae.UNetMidBlock2D import UNetMidBlock2D
from modelzoo.vision.pytorch.dit.layers.vae.UpDecoderBlock2D import (
    UpDecoderBlock2D,
)


[docs]class Decoder(nn.Module):
[docs] def __init__( self, in_channels=3, out_channels=3, up_block_types=("UpDecoderBlock2D",), block_out_channels=(64,), layers_per_block=2, norm_num_groups=32, act_fn="silu", ): super().__init__() self.layers_per_block = layers_per_block self.conv_in = nn.Conv2d( in_channels, block_out_channels[-1], kernel_size=3, stride=1, padding=1, ) self.mid_block = None self.up_blocks = nn.ModuleList([]) # mid self.mid_block = UNetMidBlock2D( in_channels=block_out_channels[-1], resnet_eps=1e-6, resnet_act_fn=act_fn, output_scale_factor=1, resnet_time_scale_shift="default", attn_num_head_channels=None, resnet_groups=norm_num_groups, temb_channels=None, ) # up reversed_block_out_channels = list(reversed(block_out_channels)) output_channel = reversed_block_out_channels[0] for i, up_block_type in enumerate(up_block_types): assert ( up_block_type == "UpDecoderBlock2D" ), f"Support for {up_block_type} not added" prev_output_channel = output_channel output_channel = reversed_block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 up_block = UpDecoderBlock2D( num_layers=self.layers_per_block + 1, in_channels=prev_output_channel, out_channels=output_channel, add_upsample=not is_final_block, resnet_eps=1e-6, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, resnet_time_scale_shift="default", ) self.up_blocks.append(up_block) prev_output_channel = output_channel # out self.conv_norm_out = nn.GroupNorm( num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6, ) self.conv_act = nn.SiLU() self.conv_out = nn.Conv2d( block_out_channels[0], out_channels, 3, padding=1 )
def forward(self, z): sample = z sample = self.conv_in(sample) # middle sample = self.mid_block(sample) # up for up_block in self.up_blocks: sample = up_block(sample) # post-process sample = self.conv_norm_out(sample) sample = self.conv_act(sample) sample = self.conv_out(sample) return sample